| 改进YOLOV3的火灾检测方法 |
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| 引用本文： | 罗小权,潘善亮. 改进YOLOV3的火灾检测方法[J]. 计算机工程与应用, 2020, 56(17): 187-196. DOI: 10.3778/j.issn.1002-8331.1912-0273 |
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| 作者姓名： | 罗小权 潘善亮 |
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| 作者单位： | 宁波大学 信息科学与工程学院，浙江 宁波 315211 |
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| 基金项目： | 浙江省公益性技术应用研究计划项目 |
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| 摘 要： | 针对传统火灾探测器检测范围有限，传统火灾检测算法精度不高、检测时间长等问题，提出一种基于改进YOLOV3的火灾检测方法YOLOV3-IMP。在YOLOV3网络结构上进行改进，包含对特征提取网络改进和多尺度检测改进，提高对浅层特征的学习能力；通过改进的K-means聚类算法生成出初始先验框；通过改进的损失函数提高对小火灾区域的检测能力；在输出火灾检测图像之前采用Softer-NMS算法，提高对重叠区域的检测能力。实验结果表明，改进的算法准确率和召回率为91.6%，83.2%，[mAP]高达84.5%，检测速度可达0.28?s，可以满足火灾检测的实时性和准确性，能够实现有效的火灾检测。
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| 关 键 词： | 火灾检测 YOLOV3-IMP 多尺度检测 Softer-NMS |
Improved YOLOV3 Fire Detection Method |
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| LUO Xiaoquan,PAN Shanliang. Improved YOLOV3 Fire Detection Method[J]. Computer Engineering and Applications, 2020, 56(17): 187-196. DOI: 10.3778/j.issn.1002-8331.1912-0273 |
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| Authors: | LUO Xiaoquan PAN Shanliang |
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| Affiliation: | School of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China |
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| Abstract: | Aiming at the problems of traditional fire detectors with limited detection range, low accuracy of traditional fire detection algorithms and long detection time, a fire detection method YOLOV3-IMP based on improved YOLOV3 is proposed. YOLOV3 network structure is improved to enhance the ability to learn shallow features, including improvements to feature extraction networks and multi-scale detection. Initial prior frames are generated through an improved [K]-means clustering algorithm. Detection capabilities are improved for small fire areas through an improved loss function. Softer-NMS algorithm is adopted before outputting fire detection images to improve detection ability for overlapping areas. Experimental results show that the improved algorithm’s accuracy and recall rate is 91.6%, 83.2%, mAP is as high as 84.5%, and the detection speed is up to 0.28?s. It can meet the real-time and accuracy of fire detection, which can achieve effective fire detection. |
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| Keywords: | fire detection YOLOV3-IMP multi-scale detection Softer-NMS |
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